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    Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models

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    Publication type
    Journal article
    Author
    Veredas, David
    Luciani, Matteo
    Publication Year
    2015
    Journal
    Journal of Forecasting
    Publication Volume
    34
    Publication Issue
    3
    Publication Begin page
    163
    Publication End page
    176
    
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    Abstract
    We introduce an approximate dynamic factor model for modeling and forecasting large panels of realized volatilities. Since the model is estimated by means of principal components and low-dimensional maximum likelihood, it does not suffer from the curse of dimensionality. We apply the model to a panel of 90 daily realized volatilities pertaining to S&P 100 from January 2001 to December 2008. Results show that our model is able to capture the stylized facts of panels of volatilities (comovements, clustering, long memory, dynamic volatility, skewness and heavy tails), and that it performs fairly well in forecasting, in particular in periods of turmoil, in which it outperforms standard univariate benchmarks. Copyright © 2015?John Wiley & Sons
    Keyword
    Accounting & Finance
    Knowledge Domain/Industry
    Accounting & Finance
    DOI
    10.1002/for.2325
    URI
    http://hdl.handle.net/20.500.12127/5219
    ae974a485f413a2113503eed53cd6c53
    10.1002/for.2325
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